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Journal of Intelligent Decision Making and Information Science

A Quantum-Enhanced Geospatial Data Processing Framework Integrating Cluster-Visualized Quantum Computing and Hybrid Deep Learning for Satellite Image Analysis

Gudlanarva Sudhakar

Published Oct 22, 2026• 0 citations
Publisher

Abstract

Geospatial analysis of satellite imagery has become indispensable for environmental monitoring, disaster management, and urban planning, yet the growing volume of Synthetic Aperture Radar (SAR) and optical Earth-observation data continues to outpace the capacity of conventional processing pipelines. Existing geospatial toolchains remain fragmented across proprietary software, demand substantial preprocessing expertise, and rely on purely classical learning algorithms whose clustering and segmentation performance degrades on high-dimensional, speckle-affected SAR feature spaces. To address these limitations, this paper presents a quantum-enhanced extension of GeoLens, an open-source Python framework for end-to-end geospatial raster processing, and proposes two novel methods: (i) Quantum Cluster-Visualized Computing (QCVC), a q-means-inspired clustering algorithm that employs amplitude encoding and SWAP-test distance estimation to generate interpretable, cluster-visualized land-cover maps from unlabeled SAR features, and (ii) QE-IAS-UNet, a hybrid quantum-enhanced Inception-Attention-Skip U-Net in which a quanvolutional front-end layer built from four-qubit variational circuits enriches multi-scale feature extraction for semantic segmentation. Experiments are designed on SAR and optical products obtained from the ISRO Bhoonidhi portal together with openly available labeled benchmarks, covering five land-cover classes. In the reported evaluation protocol, QCVC improves the mean silhouette coefficient from 0.55 to 0.61 over classical k-means at the optimal cluster number k = 5, while QE-IAS-UNet attains 96.4% overall accuracy and 95.8% F1-score, exceeding U-Net, ResUNet, PSPNet, DeepLabv3, and Attention U-Net baselines by 1.8-8.2 percentage points. The principal contributions are a unified quantum-classical geospatial processing architecture, two reproducible algorithms with explicit mathematical formulations and cutoff criteria, and a comprehensive evaluation methodology spanning accuracy, similarity, and cluster-validity metrics. Future work targets deployment on near-term quantum hardware and federated multi-sensor extension......

DOI
10.17762/jidmis.v3.5288

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